1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
20/9/2023 VTR 21990 Andrés NA
16/9/2023 Comida 27980 Tami Cajas Soul Bar
23/9/2023 Comida 57639 Tami Supermercado
24/9/2023 Diosi 8000 Andrés arena diosi 10kg
30/9/2023 Electricidad 44407 Andrés NA
30/9/2023 Comida 51726 Tami Supermercado
6/10/2023 Comida 44298 Tami Supermercado
14/10/2023 Comida 86673 Tami Supermercado
10/10/2023 Diosi 6880 Tami Omega aceite petsu
17/10/2023 Diosi 55990 Tami Comida Diosi
17/10/2023 Diosi 50000 Tami Veterinaria
20/10/2023 Comida 41970 Tami Barritas Wild Soul
21/10/2023 Diosi 56170 Andrés n y d + 2 tarrito
21/10/2023 Comida 50000 Andrés la providencia
21/10/2023 Comida 76052 Tami Supermercado
21/10/2023 Diosi 55990 Andrés Pelet SuperZoo
26/10/2023 Comida 17493 Tami Chicken love u
30/10/2023 Comida 61933 Tami Supermercado
31/10/2023 Diosi 20000 Tami Veterinaria
1/11/2023 Electricidad 44414 Andrés NA
5/11/2023 Comida 47648 Tami Supermercado
10/11/2023 Cámaras Seguridad M.Barrios 15000 Tami NA
12/11/2023 Comida 63292 Tami Supermercado
12/11/2023 Gas 78000 Andrés lipigas mas propina 1500
15/11/2023 Comida 4418 Tami Supermercado
18/11/2023 Comida 53051 Tami Supermercado
22/11/2023 Comida 7838 Tami Supermercado
23/11/2023 Comida 31470 Tami Barritas Wild Soul
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.2404e+08   2    8.0204  4e-04 ***
## lag_depvar    8.8847e+10   1 1729.4955 <2e-16 ***
## Residuals     3.2775e+10 638                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1070.094 13387.58 0.0164888
## 2-0 29081.138 23483.078 34679.20 0.0000000
## 2-1 21852.300 18548.610 25155.99 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
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## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
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## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   486 51315.40 15072.480
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1912.51994   3994.79299   -492.79336   2477.32398  -2880.09613    551.28976 
##            8            9           10           11           12           13 
##  -5607.99942  -1241.72163  -4025.66563   -534.17598  -5039.33131  -1778.77610 
##           14           15           16           17           18           19 
##  -1068.01105    223.42342  -3360.69966   -531.54025  -2261.64280   6459.25122 
##           20           21           22           23           24           25 
##  -1523.16428  -1221.82236   1450.97787  -1170.94629    235.92529   1710.21081 
##           26           27           28           29           30           31 
##  -7047.44243    872.77753   8154.55594    547.67035    116.79655  -2276.69492 
##           32           33           34           35           36           37 
##   1649.58545   4675.85934   1312.31224   2584.34022  -1644.98338   4777.89408 
##           38           39           40           41           42           43 
##   4404.00335  -2106.77034  -2875.35713  -1072.15416 -10728.10485   7101.70313 
##           44           45           46           47           48           49 
##   2529.87526   1391.37178   8152.87840    879.68314   6711.49520   6996.55747 
##           50           51           52           53           54           55 
##  -5509.82278  -4578.66355  -4958.79763  -7933.79454   5978.36248  -4093.95088 
##           56           57           58           59           60           61 
##  -4984.61924   3686.52143    812.51788    -80.60263    100.04750  -5029.84698 
##           62           63           64           65           66           67 
##  18005.29853   3873.00313  -3374.40958   6095.73527   7604.20920  15003.98953 
##           68           69           70           71           72           73 
##   2285.95345 -12662.21055  -1070.18536   4826.33478  -4653.10012  -4278.84191 
##           74           75           76           77           78           79 
## -10468.65368   2297.69086  -5500.60752    876.18851  -7009.38814    295.11211 
##           80           81           82           83           84           85 
##  -2563.45588  -2919.21018  -4177.90983   -824.64697   2054.68057   3579.47624 
##           86           87           88           89           90           91 
##    387.59522   -552.93192    128.63291   4246.98289  -1130.25299   1158.65658 
##           92           93           94           95           96           97 
##  -2035.44164  -1056.49473    148.32517    253.33508  -7496.85658   2242.35035 
##           98           99          100          101          102          103 
##  -8687.70307  -3173.85669  -4296.85030  -2035.54714  -1553.58487   2903.24480 
##          104          105          106          107          108          109 
##  -2524.67930   2391.86561  -1285.97954    838.35664   2490.55612  -3189.84079 
##          110          111          112          113          114          115 
##  -4811.74575  -1014.57146   1745.07223  11591.33406  -1113.88458   2758.77198 
##          116          117          118          119          120          121 
##   4392.14099   3695.74484   -865.25958  -4530.76838  -3648.61488   2317.49923 
##          122          123          124          125          126          127 
##  -1690.61514   1345.90556   8888.85922   1039.31068    315.05868  -2356.60776 
##          128          129          130          131          132          133 
##   2753.03702   7188.36446   1263.05743  -8260.69832   1800.79819   4213.97487 
##          134          135          136          137          138          139 
##  -3017.69613  -1349.81817   -818.36867  -3863.86131   1125.99013   -522.46485 
##          140          141          142          143          144          145 
##  -2945.49972   1637.06848  -1919.15332  -7896.63298   1836.09743  -3618.67722 
##          146          147          148          149          150          151 
##   1917.01461   -379.46880    912.44788   -436.13215   1279.18477   1148.74433 
##          152          153          154          155          156          157 
##   3346.30580  -4807.62857  -1216.60509  -3293.60615   5846.96094   9762.16858 
##          158          159          160          161          162          163 
##  -3564.74400  -4946.46016   3380.13933     77.67919   2608.96647  -5925.51568 
##          164          165          166          167          168          169 
##  -6858.62394   3946.10401  17296.32620   3868.66292   -112.45416  -2193.55818 
##          170          171          172          173          174          175 
##   -918.32270   3742.54077    -20.44155  -7888.45524   2889.75470   4409.81258 
##          176          177          178          179          180          181 
##    787.18844   8912.32888  -8939.12813  -3360.10121 -10700.82528 -11387.59712 
##          182          183          184          185          186          187 
##    911.43448   9042.54224  -1471.52610   5875.32400   6623.96616  13343.17391 
##          188          189          190          191          192          193 
##   8834.00111  -3555.80267   2838.55900  10742.75791  -1128.39864  -2020.76036 
##          194          195          196          197          198          199 
##  -9950.43794  -6253.96152   1220.99155  -5216.25817  -9868.02331   5153.32492 
##          200          201          202          203          204          205 
##  -3165.67329  -1844.71657   -942.71310   6366.27271   9890.61341    764.03005 
##          206          207          208          209          210          211 
##   3101.07644   3307.91306   6026.74148  13151.73380  -5179.03488 -10948.36467 
##          212          213          214          215          216          217 
##  -5556.82968 -10585.54872  -5253.50157   1289.18618 -13183.24328  16017.03454 
##          218          219          220          221          222          223 
##   7780.18754   1645.53841  26822.71664  13114.99644   8070.50415  14799.33795 
##          224          225          226          227          228          229 
##  -2991.33121  -1002.38349   4394.60224    969.03408   3288.86454   9532.33527 
##          230          231          232          233          234          235 
##   6456.49994  -1248.98322  -1281.14291   9877.25635 -10942.24606  -6986.41118 
##          236          237          238          239          240          241 
##  -8404.41373 -10126.50218   2884.36105   1244.76112  -8359.91456  -9183.27061 
##          242          243          244          245          246          247 
##   8775.01719  -7877.25126   2260.33956 -10448.43343  -4356.18318   1095.06574 
##          248          249          250          251          252          253 
##    749.10753 -12513.20112   3262.19510   1800.39686   4025.37077   2051.39454 
##          254          255          256          257          258          259 
##  -1190.69622  11097.78493  21042.57732   3701.08060  -3775.28341   4469.87038 
##          260          261          262          263          264          265 
##  -1306.98811   4048.29069  -4513.19695 -10679.53407  -4726.94936   -598.01233 
##          266          267          268          269          270          271 
##  -5259.73764   8630.06700  -4255.47817   4140.03754  -2075.62047   4424.25694 
##          272          273          274          275          276          277 
##    781.15990   7379.64458  -1219.57177  12169.37232  -4254.90374   1933.16572 
##          278          279          280          281          282          283 
##   -162.31332   8027.95055  -4771.19328  -2566.75322 -11162.47859  -2769.44936 
##          284          285          286          287          288          289 
##  18526.28620   7986.31160   3041.04896   -315.95275   1167.51712   6640.98886 
##          290          291          292          293          294          295 
##   7200.17506 -18381.67406 -11107.65810  -8273.24191   9405.11835   3011.44533 
##          296          297          298          299          300          301 
##  -1176.60155  27388.98959  10506.54434   5450.25201  10076.61140   3498.85039 
##          302          303          304          305          306          307 
##   -426.54171   8407.43723 -23720.60693  -3426.40769   -132.59168  -6927.65996 
##          308          309          310          311          312          313 
##  -4042.61994   2814.21062  -9239.20817  -3410.61263  -8385.10370   1271.48189 
##          314          315          316          317          318          319 
##  -3370.76476   1816.70859  -4241.61902  27250.07115   -466.28120   3505.80966 
##          320          321          322          323          324          325 
##  11072.01658   5974.62569  32807.11662   6003.13829 -20076.57611   2209.80434 
##          326          327          328          329          330          331 
##   1506.29540  -6098.64719  -1513.57020 -33097.33874    544.86082  -2562.16950 
##          332          333          334          335          336          337 
##   -336.75844  -3362.57828   3884.46866   -530.23442  -7023.55349  -3270.13979 
##          338          339          340          341          342          343 
##  -2356.04303  -7838.62291   3611.47765  -1502.20742  -1855.83691  -1106.29160 
##          344          345          346          347          348          349 
##     80.70710    417.96329  -1650.08934  -9483.53089 -13373.62754   1978.95068 
##          350          351          352          353          354          355 
##  -4557.45230  -3911.63934  -6238.86322   1450.83772   1167.81414   2601.99119 
##          356          357          358          359          360          361 
##  -3841.13625   -625.40652    588.73991   6960.84942    350.99629     42.81373 
##          362          363          364          365          366          367 
##   2665.09190  -2627.27150   -799.33114  -8674.95515  -4683.40127  -6315.39449 
##          368          369          370          371          372          373 
##  -5115.39552  -7453.52314   4748.86802    231.80038   7019.80638  -7601.36124 
##          374          375          376          377          378          379 
##  -2332.27125  -3465.46096  -2568.29537 -12565.15483   1639.38566 -10816.44731 
##          380          381          382          383          384          385 
##   5395.32280   9177.19201   3142.36154  -2324.89512   1645.52946   6816.17487 
##          386          387          388          389          390          391 
##  11585.01494  -5472.99739  -5159.20444    -53.96631   8659.50106   2033.77705 
##          392          393          394          395          396          397 
##  11444.94311  -9513.42851   2946.23449    908.62352    748.43613   -478.38755 
##          398          399          400          401          402          403 
##   -414.72007 -14360.53299   8431.86990  -1118.11194  -1322.91223   7017.19223 
##          404          405          406          407          408          409 
##  -7787.08127  -1274.42595  -2515.58687  -5825.90279  -2932.30396  -4003.94514 
##          410          411          412          413          414          415 
##  -8869.24450   5925.41959   1567.59172  -7396.43116  -7803.23620  14030.31820 
##          416          417          418          419          420          421 
##   3871.68827   4605.59425  -7862.49852  -4698.43239  -2614.48061   2787.51908 
##          422          423          424          425          426          427 
## -13981.38530  -2953.10306  -9260.41012   2759.67550   6820.21772   6557.05900 
##          428          429          430          431          432          433 
##  -3893.56845  -4082.48559  -4732.57567  -1850.16357  -5772.83690  -6745.13878 
##          434          435          436          437          438          439 
##  -6130.83673  -1618.52406  -1044.46176  -5139.39306   2384.91252   4724.66658 
##          440          441          442          443          444          445 
##  -5068.74953  -2225.69535   1503.86516  -3859.59460   2777.20658  -6570.03997 
##          446          447          448          449          450          451 
## -12182.62818  -4733.56467   9405.91095  -2077.75930   4703.46195  -5825.10633 
##          452          453          454          455          456          457 
##  -1152.33234    360.47388   3030.47017 -12201.73758   3263.40321  -6723.77756 
##          458          459          460          461          462          463 
##   6422.14266   3041.28284   2594.94742  -3714.74471   2166.95336    105.89369 
##          464          465          466          467          468          469 
##   1908.57975   -377.02453   3484.85531  -2455.84362   5942.51203  -6718.92065 
##          470          471          472          473          474          475 
##  -2854.96048  -2134.47592  -4614.65250   2990.32550   7856.83344  -5830.25775 
##          476          477          478          479          480          481 
##   1575.97133  -6057.32067  -2810.97289   2019.23382 -12872.97298  -9879.51281 
##          482          483          484          485          486          487 
##  -1435.85160   -178.90246  -1113.40755  -1466.47251  -9692.11332  10878.53768 
##          488          489          490          491          492          493 
##   6235.19109   7533.70916  -5207.86983   5502.79839   9509.92904   6399.35358 
##          494          495          496          497          498          499 
## -13065.97665 -10396.44957  -3432.56460  -1128.88796   -539.60904  -7625.77514 
##          500          501          502          503          504          505 
##    518.36029   4238.79526   5552.56362    802.32730    234.08486  -7084.16291 
##          506          507          508          509          510          511 
##    616.05051  -4978.30197   1838.59103  -1241.88744  -8108.72129   -661.21224 
##          512          513          514          515          516          517 
##  -2712.89955   -639.60312   1300.20487  -9481.11812  -7875.30137  24095.20185 
##          518          519          520          521          522          523 
##  10048.02721   6240.07471  -4915.94938   3099.76145  17341.04053  12026.73878 
##          524          525          526          527          528          529 
## -23480.58101  -4828.08856  -3589.84866   4659.58870   -192.82571 -10947.21247 
##          530          531          532          533          534          535 
##   4378.19733  13984.49178  -4683.28419   4570.66079   5809.32271  -1469.07052 
##          536          537          538          539          540          541 
##  -4272.11421  -6891.23677  -2029.16006   8373.08137    316.49649  -7956.29473 
##          542          543          544          545          546          547 
##   1873.39883   -502.08060    461.90732 -10922.24736 -11113.41049   1850.24865 
##          548          549          550          551          552          553 
##   6891.09978  -1287.88820    863.74899  -7663.82298   8518.71767   1017.14081 
##          554          555          556          557          558          559 
## -11814.53433   9114.43941   8774.50809    349.45429   5089.77844  -3278.12176 
##          560          561          562          563          564          565 
##  14329.10260  21919.72365  -5762.78615  -9153.55836   7102.24428    628.48926 
##          566          567          568          569          570          571 
##   3823.22034  -6988.97806 -17064.49232   6634.19492   6532.43457   2105.07497 
##          572          573          574          575          576          577 
##   3323.09259   2031.77870  -1891.27433  14941.63961  -9213.32843  -5995.16112 
##          578          579          580          581          582          583 
##   8852.28242   3131.77906  -6246.39816   7688.52577  -3510.94071  -2563.09375 
##          584          585          586          587          588          589 
##  15864.96752 -14101.53218   8571.87770    342.58659  -5955.40013   -604.65211 
##          590          591          592          593          594          595 
##    391.62283 -10506.77293   1784.45552  -7105.17744   3016.14176   8891.21113 
##          596          597          598          599          600          601 
##  -7322.48404   5913.61223   2899.45768   7061.23397  -2886.96899   6384.90711 
##          602          603          604          605          606          607 
##  -7979.75995   2426.94755   1475.08317   3357.31313   1754.69210    666.96906 
##          608          609          610          611          612          613 
##  -5548.85251   8235.48385   -898.79333  -2322.57027  -3251.35290  -8082.23653 
##          614          615          616          617          618          619 
##  11983.68629   5164.94278  -9017.67034  11764.52867   6371.40001  -5178.33740 
##          620          621          622          623          624          625 
##  26640.33056 -12151.72916  -6367.53928   3435.09873  -3845.10883 -10359.46375 
##          626          627          628          629          630          631 
##  11359.72802 -21381.22201  -2505.37468   8584.74795  11217.16162  -1284.03548 
##          632          633          634          635          636          637 
##  33517.76595  -5842.85867   6284.33320   6004.04840  -1635.89830  -4807.44193 
##          638          639          640          641          642          643 
##  -1532.31763 -12082.95016  -2109.52723  -1780.93559  -2433.19856  -2796.43915 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17356.77 20144.21 24308.94 24032.82 26336.81 23725.42 24426.71 19758.86 
##       10       11       12       13       14       15       16       17 
## 19500.95 16899.46 17660.62 14458.63 14508.73 15159.43 16820.41 15175.68 
##       18       19       20       21       22       23       24       25 
## 16188.64 15575.32 22509.16 21612.39 21103.16 22953.52 22293.65 22932.50 
##       26       27       28       29       30       31       32       33 
## 24739.73 18795.51 20485.44 28158.33 28214.77 27894.55 25573.70 26946.71 
##       34       35       36       37       38       39       40       41 
## 30709.12 31050.23 32429.84 29992.68 34039.00 37179.77 34297.64 31175.44 
##       42       43       44       45       46       47       48       49 
## 30047.39 20824.58 28185.55 30570.91 31637.26 38331.89 37837.08 42401.44 
##       50       51       52       53       54       55       56       57 
## 46548.82 39399.95 34082.37 29209.51 22497.78 28655.81 25308.19 21683.48 
##       58       59       60       61       62       63       64       65 
## 25999.34 27232.46 27523.24 27926.42 23883.99 40127.14 41932.41 37278.12 
##       66       67       68       69       70       71       72       73 
## 41396.79 46209.30 56653.62 54709.07 40261.90 37820.09 40774.67 35194.41 
##       74       75       76       77       78       79       80       81 
## 30742.08 21640.59 24774.89 20786.10 22828.39 17831.03 19804.17 19046.92 
##       82       83       84       85       86       87       88       89 
## 18095.05 16204.50 17455.46 20987.81 25312.83 26281.93 26306.37 26910.16 
##       90       91       92       93       94       95       96       97 
## 30948.68 29803.77 30782.16 28887.21 28103.82 28464.24 28862.29 22574.51 
##       98       99      100      101      102      103      104      105 
## 25526.27 18703.00 17583.14 15664.98 15958.44 16621.61 21000.39 20103.13 
##      106      107      108      109      110      111      112      113 
## 23540.55 23334.93 24975.87 27792.27 25342.89 21861.00 22130.64 24721.38 
##      114      115      116      117      118      119      120      121 
## 35357.88 33588.66 35387.57 38322.97 40237.83 37974.77 32904.47 29322.64 
##      122      123      124      125      126      127      128      129 
## 31361.76 29677.81 30834.57 38274.83 37924.80 37006.04 33935.39 35679.21 
##      130      131      132      133      134      135      136      137 
## 40963.80 40415.84 31802.20 33040.45 36163.27 32649.25 31070.37 30174.58 
##      138      139      140      141      142      143      144      145 
## 26803.87 28188.61 27963.07 25697.93 27679.87 26333.49 20069.90 23036.82 
##      146      147      148      149      150      151      152      153 
## 20909.13 23823.75 24352.41 25909.42 26087.67 27707.11 28980.55 31949.06 
##      154      155      156      157      158      159      160      161 
## 27514.32 26792.75 24399.32 30169.69 41585.17 39950.46 37370.72 42285.61 
##      162      163      164      165      166      167      168      169 
## 43664.60 47008.80 42569.91 37975.61 43286.96 59246.91 61412.60 59859.99 
##      170      171      172      173      174      175      176      177 
## 56752.32 55185.17 57831.01 56875.60 49329.53 52093.76 55757.81 55793.24 
##      178      179      180      181      182      183      184      185 
## 62772.41 53474.10 50293.25 41294.88 33011.85 36446.46 46337.81 45805.25 
##      186      187      188      189      190      191      192      193 
## 51633.03 57257.40 67814.00 72985.95 66813.01 67002.38 73924.26 69691.47 
##      194      195      196      197      198      199      200      201 
## 65308.30 54777.96 48933.44 50327.83 46015.02 38348.25 44638.10 42902.72 
##      202      203      204      205      206      207      208      209 
## 42548.28 43016.58 49667.96 58370.54 58007.92 59696.52 61317.54 65029.12 
##      210      211      212      213      214      215      216      217 
## 74296.89 66545.94 54982.97 49704.98 40890.36 37911.96 40960.24 31189.97 
##      218      219      220      221      222      223      224      225 
## 47807.10 54974.18 55857.14 78144.57 85482.21 87443.38 94875.33 86016.24 
##      226      227      228      229      230      231      232      233 
## 80140.68 79731.39 76451.71 75630.81 80268.36 81603.98 76156.29 71469.74 
##      234      235      236      237      238      239      240      241 
## 77004.67 63932.84 56136.56 48256.22 40043.92 44147.81 46255.34 39843.56 
##      242      243      244      245      246      247      248      249 
## 33655.84 43722.39 38090.09 41943.15 34369.47 33102.51 36681.04 39445.63 
##      250      251      252      253      254      255      256      257 
## 30467.66 36281.03 40002.63 45088.32 47749.55 47252.79 57337.42 74467.21 
##      258      259      260      261      262      263      264      265 
## 74286.14 67737.27 69187.99 65488.14 66903.91 60792.68 50292.52 46403.30 
##      266      267      268      269      270      271      272      273 
## 46608.31 42796.79 51416.05 47767.39 51827.05 49983.17 53965.13 54254.93 
##      274      275      276      277      278      279      280      281 
## 60146.00 57829.91 67299.76 61352.12 61557.74 59941.48 65563.76 59425.90 
##      282      283      284      285      286      287      288      289 
## 56061.91 45833.59 44264.00 61134.40 66548.38 66949.24 64421.05 63527.58 
##      290      291      292      293      294      295      296      297 
## 67444.54 71272.67 52668.23 42978.10 37114.88 47219.55 50393.32 49525.87 
##      298      299      300      301      302      303      304      305 
## 73214.17 79034.75 79688.39 84204.01 82440.40 77574.99 80969.04 56394.84 
##      306      307      308      309      310      311      312      313 
## 52734.45 52420.95 46341.48 43609.50 47137.21 39845.76 38594.68 33270.38 
##      314      315      316      317      318      319      320      321 
## 36975.48 36174.01 39925.05 37951.79 63196.85 61083.33 62672.84 70503.09 
##      322      323      324      325      326      327      328      329 
## 72840.31 97787.15 96198.86 72536.34 71359.42 69751.22 61871.86 59054.48 
##      330      331      332      333      334      335      336      337 
## 29633.57 33243.74 33674.04 35945.29 35299.96 40945.95 41998.98 37346.28 
##      338      339      340      341      342      343      344      345 
## 36577.19 36701.19 32118.38 37991.49 38640.98 38894.01 39751.44 41499.89 
##      346      347      348      349      350      351      352      353 
## 43283.66 43040.53 36133.20 26898.91 32131.45 31016.35 30615.01 28281.45 
##      354      355      356      357      358      359      360      361 
## 32862.19 36537.72 40907.71 39134.69 40368.55 42462.15 49702.29 50241.33 
##      362      363      364      365      366      367      368      369 
## 50438.77 52850.27 50386.47 49842.67 42642.12 39897.68 36154.82 33980.09 
##      370      371      372      373      374      375      376      377 
## 30120.56 37255.63 39494.62 47214.79 41312.84 40771.60 39339.58 38882.15 
##      378      379      380      381      382      383      384      385 
## 29941.33 34443.02 27640.39 35687.38 45803.78 49294.47 47604.04 49553.97 
##      386      387      388      389      390      391      392      393 
## 55643.70 64930.28 58283.92 52868.11 52602.50 59827.37 60339.77 68826.71 
##      394      395      396      397      398      399      400      401 
## 58160.77 59694.81 59264.14 58758.82 57277.43 56064.96 43101.13 51506.83 
##      402      403      404      405      406      407      408      409 
## 50528.20 49516.09 55783.22 48482.00 47807.59 46169.33 41937.16 40792.37 
##      410      411      412      413      414      415      416      417 
## 38896.82 33114.72 40822.55 43687.57 38471.52 33662.68 48222.74 51986.98 
##      418      419      420      421      422      423      424      425 
## 55833.93 48460.86 44861.19 43564.91 47076.24 35737.96 35472.84 29851.90 
##      426      427      428      429      430      431      432      433 
## 35324.64 43477.80 50225.57 47058.77 44188.86 41178.45 41068.98 37620.57 
##      434      435      436      437      438      439      440      441 
## 33839.84 31131.81 32674.89 34485.54 32531.94 37296.19 43371.75 40192.12 
##      442      443      444      445      446      447      448      449 
## 39904.28 42847.74 40778.08 44684.04 40030.49 31250.56 30112.37 41231.47 
##      450      451      452      453      454      455      456      457 
## 40919.68 46452.53 42180.05 42522.38 44108.96 47749.31 37835.60 42583.35 
##      458      459      460      461      462      463      464      465 
## 38102.43 45513.00 48959.34 51525.03 48323.05 50614.82 50812.13 52522.60 
##      466      467      468      469      470      471      472      473 
## 52030.72 54912.84 52297.06 57242.49 50643.53 48304.48 46920.22 43615.25 
##      474      475      476      477      478      479      480      481 
## 47292.74 54599.83 49143.46 50811.03 45708.97 44121.91 46895.54 36531.37 
##      482      483      484      485      486      487      488      489 
## 30227.71 32057.90 34698.12 36156.90 37102.54 30876.46 43144.38 49665.15 
##      490      491      492      493      494      495      496      497 
## 56352.44 51174.63 55906.50 63380.36 67111.98 53656.02 44431.14 42497.46 
##      498      499      500      501      502      503      504      505 
## 42813.89 43588.49 38190.64 40539.35 45729.86 51292.53 51987.34 52095.59 
##      506      507      508      509      510      511      512      513 
## 45929.38 47241.30 43578.84 46276.60 45949.29 39796.64 40904.04 40096.46 
##      514      515      516      517      518      519      520      521 
## 41178.94 43763.69 36753.73 32131.94 55521.40 63511.21 67087.66 60605.38 
##      522      523      524      525      526      527      528      529 
## 61916.82 75217.98 82048.58 57523.37 52500.85 49264.41 53551.68 53068.36 
##      530      531      532      533      534      535      536      537 
## 43457.52 48344.79 60740.14 55375.77 58702.25 62606.50 59720.83 54855.67 
##      538      539      540      541      542      543      544      545 
## 48454.87 47138.92 54909.79 54665.44 47381.32 49558.37 49388.66 50067.96 
##      546      547      548      549      550      551      552      553 
## 40912.84 32919.61 37170.47 45117.03 44918.25 46588.39 40723.71 49547.86 
##      554      555      556      557      558      559      560      561 
## 50678.96 40672.27 50013.35 57711.40 57089.65 60611.98 56467.90 67981.99 
##      562      563      564      565      566      567      568      569 
## 84320.93 74619.56 63422.76 67749.37 65913.07 67074.84 58821.49 43146.09 
##      570      571      572      573      574      575      576      577 
## 50007.85 55789.21 56947.19 58979.22 59612.70 56799.36 68789.33 58385.45 
##      578      579      580      581      582      583      584      585 
## 52240.00 59682.22 61154.68 54393.47 60528.65 56197.52 53304.03 66589.68 
##      586      587      588      589      590      591      592      593 
## 52323.69 59513.98 58625.40 52479.22 51798.95 52069.20 42979.69 45717.89 
##      594      595      596      597      598      599      600      601 
## 40457.00 44613.79 53193.34 46664.39 52400.54 54728.48 60278.68 56517.38 
##      602      603      604      605      606      607      608      609 
## 61230.19 52975.62 54816.20 55576.26 57836.02 58398.03 57948.42 52247.94 
##      610      611      612      613      614      615      616      617 
## 59161.51 57262.28 54420.35 51195.52 44306.03 55574.91 59380.81 50506.33 
##      618      619      620      621      622      623      624      625 
## 60690.17 64787.34 58413.67 80175.01 65609.82 58100.04 60060.97 55511.75 
##      626      627      628      629      630      631      632      633 
## 46049.84 56532.65 37496.80 37359.97 46727.55 56990.32 55075.95 83202.29 
##      634      635      636      637      638      639      640      641 
## 73594.38 75748.95 77351.90 72188.87 65060.89 61765.81 49924.53 48327.08 
##      642      643 
## 47241.91 45756.01 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8252
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    8.020389  0.5348025    3.585415
## t2* 1729.495476 21.7984771  208.917044
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.446698       8.142607   15.10371
## 2    lag_depvar 1428.918880    1739.408490 2121.04099

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Nov 27 01:10:19 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Nov 27 01:10:25 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Nov 27 01:10:32 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Nov 27 01:10:38 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Nov 27 01:10:45 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Nov 27 01:10:51 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Nov 27 01:10:58 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Nov 27 01:11:04 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Nov 27 01:11:11 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Nov 27 01:11:17 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 4.7264 5.410333 5.629750 6.5571957
Comida 366.5578 310.278417 314.087500 346.0626522
Comunicaciones 0.0000 0.000000 0.000000 0.0000000
Electricidad 31.9587 47.072333 38.297667 32.3040435
Enceres 20.2717 20.086417 17.443792 23.6850435
Farmacia 1.9980 1.831667 7.913875 8.2250870
Gas/Bencina 34.4632 44.325000 28.954333 27.1007826
Diosi 38.1981 31.180667 41.934250 39.8665000
donaciones/regalos 0.0000 0.000000 7.170083 5.9721522
Electrodomésticos/ Mantención casa 0.0000 3.944000 30.269500 18.0319130
VTR 13.1950 25.156667 22.121792 19.3964783
Netflix 4.6340 7.151583 7.090167 6.8580652
Otros 0.0000 3.151083 1.575542 0.8220217
Total 516.0029 499.588167 522.488250 534.8819348
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2169, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-12-09 00:04:58 sería de: 37.119 pesos// Percentil 95% más alto proyectado: 40.533,6

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36616.77 36614.10
Lo.80 36630.72 36625.80
Point.Forecast 37119.18 38058.95
Hi.80 39017.48 42768.55
Hi.95 40061.37 45261.67


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2744  1010.8814
## s.e.  0.1326    29.4442
## 
## sigma^2 = 27152:  log likelihood = -370.86
## AIC=747.73   AICc=748.18   BIC=753.86
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept    xreg
##       0.2475   715.9208  9.4978
## s.e.  0.1346   288.9878  9.2492
## 
## sigma^2 = 27179:  log likelihood = -370.36
## AIC=748.72   AICc=749.49   BIC=756.89
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 763.0081 675.0316 750.6628
Lo.80 878.4437 791.2810 837.7665
Point.Forecast 1096.5066 1010.8814 1030.8347
Hi.80 1314.5695 1230.4817 1268.3966
Hi.95 1430.0051 1346.7312 1415.5759


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.30             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.3       xts_0.13.1         
## [13] forecast_8.21.1     wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.3     forcats_1.0.0      
## [22] dplyr_1.1.4         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.4       tidyverse_2.0.0    
## [28] sjPlot_2.8.15       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-4       sparklyr_1.8.4      httr_1.4.7         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.1      
## [37] stringi_1.8.2       data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.9          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.4        
##  [16] askpass_1.1         timechange_0.2.0    anytime_0.3.9      
##  [19] tseries_0.10-54     colorspace_2.1-0    xfun_0.39          
##  [22] crayon_1.5.2        jsonlite_1.8.4      lme4_1.1-35.1      
##  [25] glue_1.6.2          gtable_0.3.4        emmeans_1.8.9      
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-2          
##  [31] quantmod_0.4.25     abind_1.4-5         mvtnorm_1.2-3      
##  [34] DBI_1.1.3           ggeffects_1.3.2     Rcpp_1.0.10        
##  [37] viridisLite_0.4.2   xtable_1.8-4        performance_0.10.8 
##  [40] bit_4.0.5           htmlwidgets_1.6.2   timeSeries_4031.107
##  [43] gplots_3.1.3        ellipsis_0.3.2      spatial_7.3-14     
##  [46] pkgconfig_2.0.3     farver_2.1.1        nnet_7.3-16        
##  [49] sass_0.4.5          dbplyr_2.4.0        janitor_2.2.0      
##  [52] utf8_1.2.3          tidyselect_1.2.0    labeling_0.4.3     
##  [55] rlang_1.1.2         munsell_0.5.0       cellranger_1.1.0   
##  [58] tools_4.1.2         cachem_1.0.7        cli_3.6.1          
##  [61] generics_0.1.3      sjlabelled_1.2.0    broom_1.0.5        
##  [64] evaluate_0.20       fastmap_1.1.1       yaml_2.3.7         
##  [67] knitr_1.45          bit64_4.0.5         caTools_1.18.2     
##  [70] nlme_3.1-153        slam_0.1-50         xml2_1.3.3         
##  [73] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.14    
##  [76] curl_5.1.0          bslib_0.4.2         highr_0.10         
##  [79] fBasics_4032.96     Matrix_1.6-3        its.analysis_1.6.0 
##  [82] nloptr_2.0.3        urca_1.3-3          vctrs_0.6.4        
##  [85] pillar_1.9.0        lifecycle_1.0.3     lmtest_0.9-40      
##  [88] jquerylib_0.1.4     estimability_1.4.1  bitops_1.0-7       
##  [91] insight_0.19.7      R6_2.5.1            KernSmooth_2.23-20 
##  [94] janeaustenr_1.0.0   codetools_0.2-18    assertthat_0.2.1   
##  [97] boot_1.3-28         MASS_7.3-54         gtools_3.9.5       
## [100] openssl_2.0.6       withr_2.5.2         fracdiff_1.5-2     
## [103] bayestestR_0.13.1   parallel_4.1.2      hms_1.1.3          
## [106] quadprog_1.5-8      timeDate_4022.108   minqa_1.2.6        
## [109] snakecase_0.11.1    rmarkdown_2.25      carData_3.0-5      
## [112] TTR_0.24.3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))